Speaker 1: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glapwell. This season,
Speaker 1: we're diving back into the world of artificial intelligence, but
Speaker 1: with a focus on the powerful concept of open its possibilities, implications,
Speaker 1: and misconceptions. We'll look at openness from a variety of
Speaker 1: angles and explore how the concept is already reshaping industries,
Speaker 1: ways of doing business and our very notion of what's possible.
Speaker 1: In today's episode, Jacob Goldstein sat down with Mariam Ashuri,
Speaker 1: the Director of Product Management and Head of Product for
Speaker 1: IBM's Watson x dot AI, where she spearheads the product
Speaker 1: strategy and delivery of IBM's Watson x foundation models. She
Speaker 1: is a technologist with more than fifteen years of experience
Speaker 1: developing data driven technologies. The conversation folks on how enterprises
Speaker 1: can use technology to build and deliver greater transparency in
Speaker 1: AI With Granite. Meriam explained how Grantite can be utilized
Speaker 1: to improve efficiency across various domains. She discussed how these
Speaker 1: models are being used in real world business applications, particularly
Speaker 1: in areas like customer care, where AI can help enable quick,
Speaker 1: accurate responses based on internal company data. Meriam provided a
Speaker 1: fascinating look into how enterprises have moved from mere experimentation
Speaker 1: with generative AI to actual production, navigating challenges such as
Speaker 1: increased latency, cost, and energy consumption. She highlighted how the
Speaker 1: emerging trend of smaller models customized with proprietary data can
Speaker 1: potentially deliver high performance at a fraction of the cost,
Speaker 1: marking a significant shift in how enterprises leverage AI. Whether
Speaker 1: you're an AI enthusiast, we're a business leader looking to
Speaker 1: harness the power of artificial intelligence, this episode is packed
Speaker 1: with valuable insights and forward thinking strategies.
Speaker 2: Let's just start with your background. How did you come
Speaker 2: to work at IBM.
Speaker 3: I joined IBM right after I graduated. I have an
Speaker 3: AI background, and throughout the years, I've held many roles
Speaker 3: in design, engineering, development, research, mostly focused on AI application
Speaker 3: development and design. In my current job, I'm the product
Speaker 3: owner for What's the Next DAYI, which is the IBM
Speaker 3: platform for enterprise AI. What excites me about this job,
Speaker 3: I would say, is the technology advancements over the last
Speaker 3: eighteen months in the market. We've been witnessing how generative
Speaker 3: II has been changing the market. The way that I
Speaker 3: see that is JENAI has been perhaps one of the
Speaker 3: largest paradigm shifts when we think about productivity. The same
Speaker 3: way that Internet and personal computers impacted the productivity of workforce,
Speaker 3: Now we are witnessing another wave of all those opportunities
Speaker 3: that it can unlock for especially enterprise AI when it
Speaker 3: comes to enhancing the productivity of the workforce and releasing
Speaker 3: some time that can potentially be put into creating more
Speaker 3: value work for enterprise. So that's the major part that
Speaker 3: I picked this team to have an impact on the
Speaker 3: market and the community, but also of course using the
Speaker 3: skills that I gain through all these years through IBM
Speaker 3: to help to establish IBM as the market leader for
Speaker 3: enterprise AI.
Speaker 2: So you talked about jenai as this sort of generational,
Speaker 2: transformational technological force, and I'm curious just in terms of
Speaker 2: how it's going to come into the world, Like, how
Speaker 2: do you see market adoption of genai sort of evolving
Speaker 2: from here?
Speaker 3: Well, last year was the year of excitement about generative AI.
Speaker 3: Most of the companies were experimenting and exploring with GENI.
Speaker 3: We see that energy shifted towards how to best monetize
Speaker 3: that technology. Almost half of the market has moved from
Speaker 3: investigation to pilots, ten percent has moved to production. When
Speaker 3: you're exploring with this technology, you're looking for a valve factor,
Speaker 3: You're looking for an AHA moment. That's why very large
Speaker 3: general purpose models shine. But as companies move toward production
Speaker 3: and scale, they soon realized the past success is not
Speaker 3: that straightforward. For example, they're larger the model, the larger
Speaker 3: computer resources it requires. That translates to increased latency that's
Speaker 3: your response time. That translates to increased cost. That translates
Speaker 3: to increase carbon, food print, energy consumption. So think about that.
Speaker 3: At the scale of enterprise in production, some of them
Speaker 3: can be a showstopper. Because of this reason, what actually
Speaker 3: c is emerging in the market is instead of focusing
Speaker 3: on very large general purpose models, coming back to very small,
Speaker 3: trustworthy models that they can customize on their own proprietary
Speaker 3: data that's the data about their customers, that the data
Speaker 3: about their specific domains to create something differentiated that is
Speaker 3: much smaller and delivers the performance that they want on
Speaker 3: a target use case for a fraction of the costs.
Speaker 2: Uh huh. So let's talk a little bit more specifically
Speaker 2: about what you're working on. Let's talk about Granite. First
Speaker 2: of all, tell me what is Granite.
Speaker 3: Granite is our industrial leading family of models, flagship IBM models.
Speaker 3: These are the models that we from scratch. When offered
Speaker 3: to our platform, we offer indemnification and we stand behind
Speaker 3: them today. It comes in four flavors, language, code, time series,
Speaker 3: and geospecial models. Granite language series is covering English, Spanish, German,
Speaker 3: Portuguese and Japanese. We have a combination of commercial and
Speaker 3: open source language models on Granite. For example, we recently
Speaker 3: released the Granite seven B language model, small powerful English model.
Speaker 3: On the code front, our models are state of the
Speaker 3: art models ranging from three billion to thirty four billion parameters.
Speaker 3: These are very powerful models that performs or outperforms in
Speaker 3: some cases the popular open source models in their right class.
Speaker 3: So very powerful models.
Speaker 2: So I get the idea a big picture about these models,
Speaker 2: but it would be helpful to just get a sense
Speaker 2: specifically of what they're doing. And you give me any
Speaker 2: specific examples of how these models are being used in
Speaker 2: businesses in the real world right now.
Speaker 3: Well, the top use cases for generative AI are really
Speaker 3: content generation, summarization, information extraction. Perhaps the most popular use
Speaker 3: case that we are seeing in enterprise is content grounded
Speaker 3: question and answering. So using these models as a base
Speaker 3: to connect them to a body of information let's say,
Speaker 3: their policies, their documents that is internal to the enterprise,
Speaker 3: and get the model to provide answers based on that question.
Speaker 3: One example of that is for customer agents customer care,
Speaker 3: when a customer is asking a question. Previously, the agent
Speaker 3: that responds to the customer had to answer the question
Speaker 3: and if they don't know the answer escalated to the product.
Speaker 3: Especially is keeping people on hold on the line to
Speaker 3: go figure out the answer for them and then come back.
Speaker 3: You can think of the time it takes to resolve
Speaker 3: an issue. But now we llms, we have an opportunity
Speaker 3: to automatically retrieve the information based on the internal documents
Speaker 3: of the company, formulate an answer, show it to the
Speaker 3: human agent, and then if they verify with the sources
Speaker 3: of aries coming from, they can just translate it directly
Speaker 3: to the customer. This is a very simple example of
Speaker 3: how it's impacting the customer care.
Speaker 2: So one big theme of this season is this idea
Speaker 2: of open and one of the things that's interesting to
Speaker 2: me about the work you're doing is you are using
Speaker 2: not only granted this model IBM developed, but you're also
Speaker 2: using third party models right from other places. So tell
Speaker 2: me about that work and how that is sort of
Speaker 2: fitting into your kind of real world typically enterprise GENAI work.
Speaker 3: When it comes to model strategy, our strategy is really
Speaker 3: focused on two pillars, multimodel and multidiplom It means that
Speaker 3: we don't believe one single model rules all the use cases.
Speaker 3: And I think at this point the market has also
Speaker 3: realized the enterprise markets in average today are using five
Speaker 3: to ten different models for different use cases. Oh interesting,
Speaker 3: So in our portfolio, if you look into what's on
Speaker 3: Extra DAYI today, we are offering a large sets of
Speaker 3: high performing, state of the art models coming from open
Speaker 3: source commercial models that we are bringing through our partners
Speaker 3: and also IBM developed models. In addition to all of these,
Speaker 3: we also have an option for bring your own model
Speaker 3: from outside the platform. Let's say you have a custom
Speaker 3: model that you made it yourself, you can bring it
Speaker 3: to the platform and really helping the customers to navigate
Speaker 3: through avoid range of models and pick the right model
Speaker 3: for their target use case. Throughout that we've been heavily
Speaker 3: working with our partners, and you know, this is the
Speaker 3: market that is evolving rapidly. We've been at the forefront
Speaker 3: of a spit to delivery. One example that I like
Speaker 3: to highlight is recently Metal released Lama four or five billion,
Speaker 3: such a powerful model. On the same day that it
Speaker 3: was released to the market, we made it available in
Speaker 3: our platform to our customers the same day. And not
Speaker 3: only we delivered it on the same day, we are
Speaker 3: offering competitive pricing but also for flexibility in where to deploy.
Speaker 3: So we are giving an option to enterprise to deploy
Speaker 3: these models on the platform of dat choice, either multi
Speaker 3: cloud it can be gcpaws as youre IBM cloud, or
Speaker 3: on premises. The same for mistrall Ai. Mistrall Ai recently
Speaker 3: released the model misroll large two on the same day
Speaker 3: we delivered that through the platform. That's an example of
Speaker 3: a commercial model Lama but open source, but large two
Speaker 3: is a commercial model that we made available through the platform.
Speaker 2: Great. So I want to talk about enterprise grade foundation models,
Speaker 2: just to get into it briefly, what's a foundation model.
Speaker 3: People associate foundation models with a large language model, but
Speaker 3: large language models are really a subset of foundation models.
Speaker 3: Large language models are focused on language, but foundation models
Speaker 3: can be code generators, can be focused on time series
Speaker 3: model we talked about, they can be images, it can
Speaker 3: be jewispecial models. So foundation model, as the term suggests,
Speaker 3: your foundations to create a series of subsequent models that
Speaker 3: can be customized for a downstream use case. And that's
Speaker 3: why they are calling them foundation models. LM is a
Speaker 3: good example of that as a subset for language that
Speaker 3: you can further customize on your specific data to get
Speaker 3: the model to do other works. So the core of
Speaker 3: these foundation models, they are basically trained on an ab
Speaker 3: third amount of data r data sets that of the
Speaker 3: institutions today are sourcing them from the Internet, So you
Speaker 3: can imagine what can potentially go to those models, and
Speaker 3: then it comes to the enterprise and they start using it.
Speaker 3: So for us also, when we started looking into in particular,
Speaker 3: it was triggered by customers asking us to provide client
Speaker 3: protections on these models, and we started thinking about, let's
Speaker 3: look into how the models are trained and if we
Speaker 3: are comfortable of fering client protections on the models that
Speaker 3: are available in the market. And guess what, for a
Speaker 3: majority of these models. There is absolutely no visibility into
Speaker 3: what data vent into those models, not much transparency into
Speaker 3: how the model trains, and the responsibility lies on you
Speaker 3: as the customers we start using those models.
Speaker 2: So just to be clear, that is presenting like potential risk,
Speaker 2: real potential risk to a company that is using these models.
Speaker 3: It is. It is a potential risk in particular for
Speaker 3: the customers in highly regulated industries. So what we did
Speaker 3: for Granite was when we started training these models from scratch,
Speaker 3: Basically we went to the corpus of data that was
Speaker 3: available to us. So, for example, the very first version
Speaker 3: of Granite was exposed to twenty persons of its data
Speaker 3: from finance and legal because we have a lot of
Speaker 3: financial institutions as our clients. We worked directly with our
Speaker 3: IBM research to identify detectors for harmful information like haytype
Speaker 3: use and profanity detectors.
Speaker 2: Okay, so we're talking about Granted, we're talking about this
Speaker 2: set of models IBM has developed. Let's talk about using
Speaker 2: Granite on Watson X compared to downloading open source models,
Speaker 2: Like how do those differ?
Speaker 3: By using Granite and Whatson X, you get two things.
Speaker 3: The first one is the client protection and in themification
Speaker 3: that we talked about. You get that if the model
Speaker 3: is consumed through our platform. And the second one is
Speaker 3: really the equos of platform capabilities that we are offering
Speaker 3: to help you create value on top of those data,
Speaker 3: so for example, bringing your data to customize granted for
Speaker 3: your own specific use case. But also one thing that
Speaker 3: I like to highlight in particular is the AI governance.
Speaker 3: So when you get one of these pre train models,
Speaker 3: you put it in front of your own users. Through
Speaker 3: the input and instructions that the user provides for the model,
Speaker 3: they can nodge the model to potentially create undesired behavior
Speaker 3: and change the behavior of the model. And because of
Speaker 3: this is extremely important to automatically document the lineage of
Speaker 3: who touched the model at one point, so if something happens,
Speaker 3: you can trace it back and see where it's coming from.
Speaker 3: And that's what's an extra governance is offering automatically documenting
Speaker 3: the lineage. When you use the grantite within the platform,
Speaker 3: you get all of those you can have the end
Speaker 3: to end governance, you can and have access to all
Speaker 3: these scalable deployment opportunities that is available for you, like
Speaker 3: to allow you deploy them on the platform of your
Speaker 3: choice that we talked about, either multiple cloud or on
Speaker 3: prem and it also helps you to have access to
Speaker 3: avoid range of model customizations approaches, promptuning, fine tuning, retrieval,
Speaker 3: augmented generations agents. There is a series of them available
Speaker 3: to use an apply to your model.
Speaker 1: This distinction between large language models and foundation models is
Speaker 1: eye opening. Miriam emphasized that foundation models can be tailored
Speaker 1: to specific tasks, but with that versatility comes a significant
Speaker 1: challenge the lack of transparency and how these models are trained.
Speaker 1: This composed a real risk, especially in highly regulated industries
Speaker 1: like finance. Essentially, by using Granite and watsonex together, enterprises
Speaker 1: get powerful and customizable tools.
Speaker 2: So let's talk about the future a little bit. What
Speaker 2: do you think are some of the big developments we're
Speaker 2: likely to see in the realm of AI models?
Speaker 3: Very good question. I feel like the generative AI of
Speaker 3: the past was powered by large language models. The generative
Speaker 3: AI of the future is going to reason, plan, act
Speaker 3: and reflect.
Speaker 2: Huh, and so I mean in the context of Granite
Speaker 2: in particular, like, what are we likely to see both
Speaker 2: you know, in the near term and in the sort
Speaker 2: of medium to long term.
Speaker 3: There are multiple elements to implement an agentic workflow that
Speaker 3: I just mentioned. One element of that is the LLM
Speaker 3: itself to be able to do the planning and reasoning
Speaker 3: and acting and doing something that we call tool calling.
Speaker 3: So basically, a series of tools are available to the model.
Speaker 3: You ask the model to call those and make a call.
Speaker 3: For example, we can say, hey, Granted, what is the
Speaker 3: weather like where Jacob lives. It's connect to web search API,
Speaker 3: look up your location. Then it's going to connect to
Speaker 3: weather API, calculate the weather and come back and formulate
Speaker 3: an answer and respond to that. So during this process,
Speaker 3: it first has to plan the task of how to
Speaker 3: answer that question, look into what are the tools that
Speaker 3: are available to it, and call them, and that's an
Speaker 3: ability of the model to do that. What we did
Speaker 3: with Granted was we expanded the granite capabilities to be
Speaker 3: able to do function callings. So for example, today we
Speaker 3: have an open source granted to an eb function calling
Speaker 3: that is available on hugging face to try on and
Speaker 3: you can grab the model and the model has capability
Speaker 3: to do the tool calling. I'm anticipating that in the
Speaker 3: near future the planning and reasoning and acting and reflecting
Speaker 3: capabilities of the large language models are going to continue
Speaker 3: to evolve.
Speaker 2: So thinking now from the point of view of buyers
Speaker 2: and users of AIS, really people who are listening kind
Speaker 2: from that perspective, as people are evaluating AI tools and solutions,
Speaker 2: what is the most important thing they should be thinking about?
Speaker 2: How do you think about kind of that process?
Speaker 3: I think they should always start with the area at
Speaker 3: which they think it would benefit from AI, and then
Speaker 3: within that area, look into what data they have available
Speaker 3: to potentially fit into those AI service architects do they
Speaker 3: have access to quality data? And the second question that
Speaker 3: they have to ask themselves is do I have a
Speaker 3: trusted partner that can supply what I need to be
Speaker 3: able to implement AI. That can be a collection of
Speaker 3: the foundation models that you're going to need, that can
Speaker 3: be a collection of the platform capabilities that the trusted
Speaker 3: partner can offer you to implement such a The thirting
Speaker 3: is go and evaluate the regulations. Does regulation allow you
Speaker 3: to applyoy AI to the specific area that you are
Speaker 3: investigating and you're targeting for AI. And the last part,
Speaker 3: but not least, is back to the principles of design thinking,
Speaker 3: what is the problem in that area? I'm solving with AI,
Speaker 3: and if AI is even appropriate, because we want to
Speaker 3: make sure that you use AI not just because it's
Speaker 3: a cool, hot toy in the market, but you are
Speaker 3: convinced that it can significantly enhance the user experience of
Speaker 3: your customers in that area. And once you have an
Speaker 3: answer to those all these four questions, then maybe you
Speaker 3: have a good candidates to start applying aiit.
Speaker 2: And what about from the side of project managers who
Speaker 2: are trying to just keep up with how fast things
Speaker 2: are changing, how fast innovation is happening, Like, what advice
Speaker 2: would you give those people?
Speaker 3: My advice would be focused on agility. This is a
Speaker 3: market that is evolving rapidly and the winners of the
Speaker 3: market would be those that are able to take advantage
Speaker 3: of the best the market can offer at any point
Speaker 3: of time. So in order to do that, they need
Speaker 3: to be open to experimentation, continuous learning, and to rapidly
Speaker 3: adopting the new ideas.
Speaker 2: And when you think about the future and GENAI, is
Speaker 2: there a particular, say problem that you are most excited
Speaker 2: to solve.
Speaker 3: I think that would be productivity. If you look into
Speaker 3: the stats that are out there, there are surveys that
Speaker 3: confirm that sixty to seventy percents of the time of
Speaker 3: our employees can be potentially enhanced to the productivity gains
Speaker 3: of generative I for example, I personally myself use my
Speaker 3: product for content generation a lot, so the time that
Speaker 3: it frees up can be potentially put into generating a
Speaker 3: higher value work. And because of that, I'm super excited
Speaker 3: with all the opportunities that it represents for enterprises to
Speaker 3: go and dedicate the time of their employees to higher
Speaker 3: value items.
Speaker 2: Great. Okay, a couple of Granite specific questions. So what
Speaker 2: are like the key things you want the world to
Speaker 2: know about Granite.
Speaker 3: Granite is open, trusted, and targeted. Two ways to think
Speaker 3: about openness. One open as open weights it's available for
Speaker 3: public to download, and the second one is open as
Speaker 3: in there is less restrictions on how the customers can
Speaker 3: legally use these models for a range of use cases.
Speaker 3: We have released Granite open source models on their Apache
Speaker 3: license that is enabling a large range of use cases.
Speaker 3: The second one was trusted. We talked about that like
Speaker 3: it's rooted in the trustworthy governance process that we established
Speaker 3: thereund how we are training these models and the responsibility
Speaker 3: that we take for these models. And the third one
Speaker 3: is targeted, targeted for enterprise. We talked about like exposing
Speaker 3: Granted to enterprise data or the domain specific Granted some
Speaker 3: of them like Cobalt Java Translation that is targeting to
Speaker 3: solve specific enterprise needs. And that's Granite, so open, trusted
Speaker 3: and targeted.
Speaker 2: So there are a lot of models out in the
Speaker 2: world all of a sudden, right, it's a crowded market.
Speaker 2: Where does Granite fit in that universe? What is the
Speaker 2: market for granted?
Speaker 3: We talked about the enterprise market shifting away from very
Speaker 3: large general purpose models to targeted, smaller models, and Granted
Speaker 3: is a small model that enterprise can pick up and
Speaker 3: customize on their proprietary data to create something that is
Speaker 3: differentiated for a target use case. So Granted is alsuited
Speaker 3: as a small domain specific business ready tailored for business
Speaker 3: and trained on enterprise data to solve enterprise questions.
Speaker 2: You mentioned small as one of the things that granted
Speaker 2: is why is that useful in some contexts for enterprise
Speaker 2: for businesses.
Speaker 3: The larger the model, the larger computer resources it requires,
Speaker 3: it translates to increased latency. That's your response time. It
Speaker 3: translates to increased cost and in translates to increased carbon
Speaker 3: footprint and energy consumption. So at the scale of enterprise transactions,
Speaker 3: when you move to production and you want to scale,
Speaker 3: some of these challenges can be multiple times stronger. Like
Speaker 3: costs can add up, the energy assumption can be a
Speaker 3: serious thing, and the latency is depending on the application,
Speaker 3: can be a showstopper and blocker because for longer, larger models,
Speaker 3: more powerful models, it just takes a way longer time
Speaker 3: to process and calculate the output.
Speaker 2: For you, we are going to finish up with a
Speaker 2: speed round and I want you to just answer with
Speaker 2: the first thing that comes to mind. Don't overthink this, Okay,
Speaker 2: complete this sentence. In five years, AI.
Speaker 3: Will be invisible.
Speaker 2: Ah, I like that. What do you mean by that?
Speaker 3: Today? AI is everywhere, But if you ask my kids
Speaker 3: at home, they know AI. But if you say very a,
Speaker 3: I like, how do you use AI? They don't know
Speaker 3: the answer because it's so blended in their life that
Speaker 3: they don't feel like it's something that they are using.
Speaker 3: They are getting used to that. So when I think
Speaker 3: of next generation and the years to that generation is
Speaker 3: so used to AI being part of their life that
Speaker 3: they feel like it's just there. That's one, and the
Speaker 3: second one is the simplicity of interaction with AI, that
Speaker 3: you don't feel like you're interacting with the system. It's
Speaker 3: just there. Like you talk to AI. Everything is automated.
Speaker 3: So I would say the simplicity and being blended to
Speaker 3: solve the right problems is the part that I'm referring
Speaker 3: to as invisible. Like Internet is everywhere and it's invisible.
Speaker 3: But we used to dial in, like you remember the
Speaker 3: dialing zone to connect the Internet. It's gone. Internet is
Speaker 3: completely invisible today.
Speaker 2: Right, Like we used to talk about logging on, right,
Speaker 2: and you don't log on anymore because you're always logged on.
Speaker 3: Yep, you're always connected.
Speaker 2: Yeah. What's the number one thing that people misunderstand about AI?
Speaker 3: AI is an irritable but should not be feared.
Speaker 2: What advice would you give yourself ten years ago to
Speaker 2: better prepare you for today?
Speaker 3: I would say, develop a broad range of skills. Even
Speaker 3: if you think they will not help you today, they
Speaker 3: may be valuable in the future.
Speaker 2: So on the consumer side, right now we hear a
Speaker 2: lot about chatbots and image generators. But on the business side,
Speaker 2: what do you think is the next big business application.
Speaker 3: AI influencers generating content.
Speaker 2: Huh. How do you use AI in your day to
Speaker 2: day life today?
Speaker 3: One simple example is LinkedIn posts. I love it to
Speaker 3: just go to my product. I'll give you an example
Speaker 3: which is my favorite one. Lama three point one four
Speaker 3: A five b the post that I announced on LinkedIn
Speaker 3: on hey, IBM is releasing the model on the same
Speaker 3: day it was generated by Lama three point one four
Speaker 3: A five billion. So using the same model to post
Speaker 3: the generate the announcement note very elegant.
Speaker 2: Is there anything else I should ask you?
Speaker 3: Oh, we didn't talk about instruct lab. So when you
Speaker 3: grab a model, you start from the model, but you
Speaker 3: need to then customize it on your proprietary data to
Speaker 3: create value on top of that. So instruct lab is
Speaker 3: giving you a method based on open source contributions to
Speaker 3: collectively contribute to improve the base model. So if you're
Speaker 3: an enterprise, you can leverage your internal employees to collectively
Speaker 3: all contribute to improve the model. And I'll give you
Speaker 3: an example of why it matters. Like if you go
Speaker 3: to higging face today and look for Lama, there are
Speaker 3: about fifty thousand different lamas coming up and the reason
Speaker 3: is because there is no way to contribute to the
Speaker 3: base model. If you're a developer, you have to make
Speaker 3: a colon of the copy of the model and find
Speaker 3: you need for your own purpose. We are the method
Speaker 3: that we call instruct lab to be able to collectively
Speaker 3: collect all that information and contribute to the base model
Speaker 3: and enhance. So that's instruct lab. I just wanted to
Speaker 3: highlight the value of being open because that's another topic
Speaker 3: that has been emerging in the market over the past
Speaker 3: eighteen months. In particular, I believe the future of AI
Speaker 3: is open, and we've been seeing how the open source
Speaker 3: markets has been changing, how the models are accessible to
Speaker 3: a wider audience, and good things typically happen when you
Speaker 3: make technology pieces accessible to a broader range of community
Speaker 3: to stress test that and that's the direction that we've
Speaker 3: been adopting with granted, and I feel like that's really
Speaker 3: the adoption that the market is going to emerge to
Speaker 3: moving forward.
Speaker 2: Yeah, this interesting, I think, maybe naively unintuitive, but it
Speaker 2: makes sense once you think about it. Thing that open
Speaker 2: source things are safer. Evily think, oh no, put it
Speaker 2: in a box so nobody can see it, and that'll
Speaker 2: be safer. But like it turns out of the world,
Speaker 2: if you let everybody poke at it, the world will
Speaker 2: find the vulnerabilities for you and you can fix them.
Speaker 3: Right, That's exactly what's going to happen. Yeah.
Speaker 2: Great, it was lovely to talk with you. Thank you
Speaker 2: so much for your time.
Speaker 3: The same here, thanks Jacob.
Speaker 1: And that wraps up this episode. A huge thanks to
Speaker 1: Mariam and Jacob. Today's conversation open my eyes as to
Speaker 1: how open technology and AI are intersecting to create more
Speaker 1: transparent and efficient systems for enterprises. From the power of smaller,
Speaker 1: more targeted models like granted to the importance of trust
Speaker 1: and governance in AI, these developments are reshaping how businesses
Speaker 1: operate at their core. As we continue to unpack the
Speaker 1: complexities of artificial intelligence, it's clear that openness, whether in data,
Speaker 1: technology or collaboration, is not just a concept, but a
Speaker 1: driving force that can unlock new possibilities. Smart Talks with
Speaker 1: IBM is produced by Matt Romano, Joey fish Ground, Amy
Speaker 1: Gains McQuaid, and Jacob Goldstein, who are edited by Lydia
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